Dataset Information

Spatial omics based machine learning (SOML) algorithms for the early detection of hepatocellular carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) is the seventh most common cancer in the world and accounts for over 800,000 deaths annually, making it the second most lethal cancer. Poor chemotherapeutic agents against HCC have necessitated the importance of early detection and surgical intervention in patient management. However, the search for biomarkers of HCC have proved difficult, as serum and tissue proteomics, transcriptomics and genomics have proved inconclusive. We have utilized a novel spatial-omics based glycan imaging method to identify glycan changes that occur directly in HCC tissue. Glycan changes included branched, fucosylated and high mannose glycan. Importantly, using matching serum samples, we were able to identify the glycans that are altered in tissue and also in the serum of individuals with HCC. Glycoproteomics was used to identify the proteins containing these glycan structures. To translate these findings, we utilized a novel multi-plexed antibody panel based glycan imaging method, which allowed us to examine all the glycans associated with the identified glycoproteins. These glycan changes, found in serum, and directly associated with the tumor, were used to create a diagnostic algorithm to detect HCC. These spatial omics based, machine learning (SOML) algorithms were tested in a discovery set (n=201) and an external validation set (n=192). AUROC values ranged from 0.95 in the discovery set and 0.91 in the independent external validation set. In conclusion, we present the development and application of a new biomarker platform that can be used to examine the glycans associated with disease.

INSTRUMENT(S): Orbitrap Exploris 480

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Liver, Blood Serum

DISEASE(S): Hepatocellular Carcinoma

SUBMITTER: Jennifer Bethard

LAB HEAD: Anand S. Mehta1

PROVIDER: PXD047546 | Pride | 2025-05-06

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	09062023_Mehta_GR10000524_DDRC_Sample10_0543_HCC.raw	Raw
	09062023_Mehta_GR10000524_DDRC_Sample1_480_cirrhotic.raw	Raw
	09062023_Mehta_GR10000524_DDRC_Sample2_491_cirrhotic.raw	Raw
	09062023_Mehta_GR10000524_DDRC_Sample3_554_cirrhotic.raw	Raw
	09062023_Mehta_GR10000524_DDRC_Sample4_561_cirrhotic.raw	Raw

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Publications

Spatial omics-based machine learning algorithms for the early detection of hepatocellular carcinoma.

Wang Mengjun M Grauzam Stephane S Bayram Muhammed Furkan MF Dressman James J DelaCourt Andrew A Blaschke Calvin C Liang Hongyan H Scott Danielle D Huffman Gray G Black Alyson A Ochoa-Rios Shaaron S Lewin David D Angel Peggi M PM Drake Richard R RR Ball Lauren L Bethard Jennifer J Castellino Stephen S Kono Yuko Y Kubota Naoto N Hoshida Yujin Y Quirk Lisa L Yopp Adam A Gopal Purva P Singal Amit A Mehta Anand S AS

Communications medicine 20241203 1

<h4>Background</h4>Worldwide, hepatocellular carcinoma (HCC) is the second most lethal cancer, although early-stage HCC is amenable to curative treatment and can facilitate long-term survival. Early detection has proved difficult, as proteomics, transcriptomics, and genomics have been unable to discover suitable biomarkers.<h4>Methods</h4>To find new biomarkers of HCC, we utilized a spatial omics N-glycan imaging method to identify altered glycosylation in cancer tissue (n = 53) and in paired se ...[more]

PMID: 39627514

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